Neurofeedback Training Research Articles

Comparison of cognitive functional therapy and neurofeedback training on kinetic gait in patients with chronic non-specific low back pain: a randomised controlled trial

Purpose of the article Walking disorders are a significant issue for patients with low back pain. The aim of clinical trials is to compare the effects of cognitive functional therapy (CFT) and neurofeedback training (NFBT) on gait kinetics in chronic non-specific low back pain (CNSLBP) patients. Materials and Methods Sixty females with chronic non-specific low back pain were recruitment for clinical trials. They were randomly divided into experimental and one control groups (Each group 20 patients). The experimental group received the relevant interventions for eight weeks. The primary outcome was pain, kinesiophobia and disability. The secondary outcome was vertical ground reaction force (VGRF) parameters. Two-Way Repeated Measures ANOVA statistical method was used for data analysis. Results Within-group comparisons showed that neurofeedback training and cognitive functional therapy groups experienced significant improvement in pain intensity, disability and kinesiophobia after eight-week (p < 0.05). However, the cognitive functional therapy group improved the vertical ground reaction force parameters better than the neurofeedback training group (p < 0.05). Conclusions cognitive functional therapy intervention had a greater effect on the vertical ground reaction force parameters. The reason for the greater effect of cognitive functional therapy intervention on vertical ground reaction force parameters can be partially explained due to the multimodal therapy used through cognitive exercises and motor control.

Brain computer interfaces for cognitive enhancement in older people - challenges and applications: a systematic review

BackgroundBrain-computer interface (BCI) offers promising solutions to cognitive enhancement in older people. Despite the clear progress received, there is limited evidence of BCI implementation for rehabilitation. This systematic review addresses BCI applications and challenges in the standard practice of EEG-based neurofeedback (NF) training in healthy older people or older people with mild cognitive impairment (MCI).MethodsArticles were searched via MEDLINE, PubMed, SCOPUS, SpringerLink, and Web of Science. 16 studies between 1st January 2010 to 1st November 2024 are included after screening using PRISMA. The risk of bias, system design, and neurofeedback protocols are reviewed.ResultsThe successful BCI applications in NF trials in older people were biased by the randomisation process and outcome measurement. Although the studies demonstrate promising results in effectiveness of research-grade BCI for cognitive enhancement in older people, it is premature to make definitive claims about widespread BCI usability and applicability.SignificanceThis review highlights the common issues in the field of EEG-based BCI for older people. Future BCI research could focus on trial design and BCI performance gaps between the old and the young to develop a robust BCI system that compensates for age-related declines in cognitive and motor functions.

Feasibility of real-time fMRI neurofeedback for rehabilitation of reading deficits in aphasia.

Reading impairments, a common consequence of stroke-induced aphasia, significantly hinder life participation, affecting both functional and leisure activities. Traditional post-stroke rehabilitation strategies often show limited generalization beyond trained materials, underscoring the need for novel interventions targeting the underlying neural mechanisms. This study investigates the feasibility and potential effectiveness of real-time functional magnetic resonance imaging (fMRI) neurofeedback (NFB) intervention for reading deficits associated with stroke and aphasia. We enrolled left-hemisphere stroke survivors in the subacute recovery period and healthy controls in an fMRI NFB intervention study focusing on increasing activation within the left supramarginal gyrus (SMG), a critical region for reading supporting orthography-phonology conversion. Preliminary findings demonstrate that stroke participants showed significant improvements in reading comprehension and phonological awareness, as evidenced by marked gains on the Reading Comprehension Battery for Aphasia (RCBA) and a phonology two-alternative forced choice test. Functional MRI results indicated that stroke participants exhibited increased activation from day 1 to day 3 of NFB training within the left SMG and the broader left hemisphere reading network, particularly during challenging nonword reading tasks. Healthy controls also showed increased activation during NFB regulation and reading tasks, but these changes were outside the traditional reading network, involving regions associated with cognitive control, reward anticipation, and learning. In both stroke participants and healthy controls, we also found changes in dynamic functional connectivity of multiple resting state networks from before to after NFB training. Although preliminary, this research contributes to the development of biologically informed interventions for reading deficits in aphasia, representing an early step towards improving post-stroke rehabilitation outcomes. Future randomized controlled trials are necessary to validate these findings by including a sham NFB control group within a larger participant sample. The study was preregistered on ClinicalTrials.gov, NCT# NCT04875936.

The effects of neurofeedback training on behavior and brain functional networks in children with autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with an unclear pathogenesis to date. Neurofeedback (NFB) had shown therapeutic effects in patients with ASD. In this study,we analyzed the brain functional networks of children with ASD and investigated the impact of NFB targeting the beta rhythm training on these networks. The Autism Behavior Checklist (ABC) and Social Response Scale (SRS) were employed to evaluate the effects of NFB training on the behavioral abilities of children with ASD. We compared the differences in static and dynamic brain functional networks between ASD and Typically Developing (TD) children, also explored the changes in these networks in ASD children after 20 sessions of NFB training. The Weighted Phase Lag Index (wPLI) was used to construct static functional networks, and the Fuzzy Entropy (FuzzyEn) algorithm was further employed to measure the complexity of static functional connectivity and construct dynamic functional networks. This allowed the analysis of functional connectivity and fluctuations in the static functional networks of ASD and TD children, as well as the time variability of the dynamic functional networks. Additionally, the study explored the changes in brain functional networks and behavioral scales before and after NFB training. Results from behavioral scales indicated significant improvements in cognitive, communication, language, and social scores in ASD children following NFB intervention. EEG analysis revealed that static functional connectivity was lower, connectivity variability was higher, and temporal variability was greater in ASD children compared to TD children. Following NFB training, increased functional connectivity, reduced connectivity variability in the Delta frequency band, and decreased temporal variability were observed in ASD children. The results revealed abnormalities in both static and dynamic functional networks in children with ASD, with NFB training showed potential to modulate these networks. While our results showed that NFB training can assist participants in regulating connectivity and temporal variability in specific brain regions, robust evidence for its effectiveness in alleviating core symptoms of ASD remained limited.

Neurofeedback and attention modulate somatosensory alpha oscillations but not pain perception.

Pain is closely linked to alpha oscillations (8 < 13 Hz) which are thought to represent a supra-modal, top-down mediated gating mechanism that shapes sensory processing. Consequently, alpha oscillations might also shape the cerebral processing of nociceptive input and eventually the perception of pain. To test this mechanistic hypothesis, we designed a sham-controlled and double-blind electroencephalography (EEG)-based neurofeedback study. In a short-term neurofeedback training protocol, healthy participants learned to up- and down-regulate somatosensory alpha oscillations using attention. Subsequently, we investigated how this manipulation impacts experimental pain applied during neurofeedback. Using Bayesian statistics and mediation analysis, we aimed to test whether alpha oscillations mediate attention effects on pain perception. The results showed that attention and neurofeedback successfully up- and down-regulated the asymmetry of somatosensory alpha oscillations. However, attention and neurofeedback did not modulate pain ratings or related brain responses. Accordingly, somatosensory alpha oscillations did not mediate attention effects on pain perception. Thus, our results challenge the hypothesis that somatosensory alpha oscillations shape pain perception. A causal relationship between alpha oscillations and pain perception might not exist or be more complex than hypothesized. Trial registration: Following Stage 1 acceptance, the study was registered at ClinicalTrials.gov NCT05570695.

Treating Adolescent Depression: Exploring Neurofeedback Training, Dhikr, and Community Stigma in Complementary Therapies (A Case Study)

Adolescent depression, particularly when complicated by psychotic features, presents significant challenges in treatment due to its complex and multifaceted nature. This case study explores the integration of neurofeedback training and Dhikr, an Islamic meditative practice, as complementary therapies alongside traditional pharmacotherapy in treating a 15 years old male diagnosed with severe recurrent depression. Despite initial reluctance and skepticism influenced by community stigma surrounding mental health and spiritual practices, both therapies contributed to noticeable improvements in the patient's symptoms and overall well-being. Neurofeedback was utilized to regulate brainwave activity, while Dhikr provided a meditative and spiritual component that promoted mindfulness and inner peace. The case highlights the importance of addressing community stigma, personalized treatment plans, and holistic approaches in managing adolescent depression, ultimately leading to better engagement and therapeutic outcomes.

Clinical response to neurofeedback in major depression relates to subtypes of whole-brain activation patterns during training.

Major Depressive Disorder (MDD) poses a significant public health challenge due to its high prevalence and the substantial burden it places on individuals and healthcare systems. Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) shows promise as a treatment for this disorder, although its mechanisms of action remain unclear. This study investigated whole-brain response patterns during rtfMRI-NF training to explain interindividual variability in clinical efficacy in MDD. We analyzed data from 95 participants (67 active, 28 control) with MDD from previous rtfMRI-NF studies designed to increase left amygdala activation through positive autobiographical memory recall. Significant symptom reduction was observed in the active group (t = -4.404, d = -0.704, p < 0.001) but not in the control group (t = -1.609, d = -0.430, p = 0.111). However, left amygdala activation did not account for the variability in clinical efficacy. To elucidate the brain training process underlying the clinical effect, we examined whole-brain activation patterns during two critical phases of the neurofeedback procedure: activation during the self-regulation period, and transient responses to feedback signal presentations. Using a systematic process involving feature selection, manifold extraction, and clustering with cross-validation, we identified two subtypes of regulation activation and three subtypes of brain responses to feedback signals. These subtypes were significantly associated with the clinical effect (regulation subtype: F = 8.735, p = 0.005; feedback response subtype: F = 5.326, p = 0.008; subtypes' interaction: F = 3.471, p = 0.039). Subtypes associated with significant symptom reduction were characterized by selective increases in control regions, including lateral prefrontal areas, and decreases in regions associated with self-referential thinking, such as default mode areas. These findings suggest that large-scale brain activity during training is more critical for clinical efficacy than the level of activation in the neurofeedback target region itself. Tailoring neurofeedback training to incorporate these patterns could significantly enhance its therapeutic efficacy.

The Effect of Left Temporal EEG Neurofeedback Training on Cerebral Cortical Activity and Precision Cognitive-Motor Performance

ABSTRACT Objectives: The present study employed individualized magnitudes of electroencephalographic (EEG) alpha (8–12 hz) power in the left temporal (T3) region as a neurofeedback target parameter during the aiming period in pre-elite air pistol shooters to determine its effectiveness on cerebral cortical activation and performance accuracy compared to physical skill training, only. Method: Shooting scores and EEG activity in the left and right temporal regions were collected from 20 healthy air pistol shooters (10 assigned to neurofeedback training) before and after a 16-session intervention completed within 6 weeks. Specifically, EEG low-alpha (8–10 hz), high-alpha (10–13 hz) power, and coherence obtained at the T3-Fz and T4-Fz recording sites over three consecutive 1-s intervals prior to trigger pull, were subjected to three separate 2 × 2 × 2 × 3 (Group x Hemisphere x Time x Epoch) ANOVAs. Results: The neurofeedback group exhibited elevated low- and high-alpha EEG power across both temporal regions, but no differences in EEG broad-band alpha coherence, accompanied by enhanced performance after the intervention compared to the control group. Conclusions: The findings support the influence of neurofeedback training on cerebral cortical arousal and performance of a precision-aiming task, however, the influence of the neurofeedback on brain dynamics (i.e. alpha power) extended beyond the targeted region as a nonspecific manifestation of cerebral cortical inhibition leading to neural efficiency at the homologous sites.

Comparison of EEG Signal Spectral Characteristics Obtained with Consumer- and Research-Grade Devices.

Electroencephalography (EEG) has emerged as a pivotal tool in both research and clinical practice due to its non-invasive nature, cost-effectiveness, and ability to provide real-time monitoring of brain activity. Wearable EEG technology opens new avenues for consumer applications, such as mental health monitoring, neurofeedback training, and brain-computer interfaces. However, there is still much to verify and re-examine regarding the functionality of these devices and the quality of the signal they capture, particularly as the field evolves rapidly. In this study, we recorded the resting-state brain activity of healthy volunteers via three consumer-grade EEG devices, namely PSBD Headband Pro, PSBD Headphones Lite, and Muse S Gen 2, and compared the spectral characteristics of the signal obtained with that recorded via the research-grade Brain Product amplifier (BP) with the mirroring montages. The results showed that all devices exhibited higher mean power in the low-frequency bands, which are characteristic of dry-electrode technology. PSBD Headband proved to match BP most precisely among the other examined devices. PSBD Headphones displayed a moderate correspondence with BP and signal quality issues in the central group of electrodes. Muse demonstrated the poorest signal quality, with extremely low alignment with BP. Overall, this study underscores the importance of considering device-specific design constraints and emphasizes the need for further validation to ensure the reliability and accuracy of wearable EEG devices.

Neurofeedback-induced desynchronization of sensorimotor rhythm elicits pre-movement downregulation of intracortical inhibition that shortens simple reaction time in humans: A double-blind, sham-controlled randomized study

Abstract Sensorimotor rhythm event-related desynchronization (SMR-ERD) is associated with the activities of cortical inhibitory circuits in the motor cortex. The self-regulation of SMR-ERD through neurofeedback training has demonstrated that successful SMR-ERD regulation improves motor performance. However, the training-induced changes in neural dynamics in the motor cortex underlying performance improvement remain unclear. Here, we hypothesized that SMR-neurofeedback based on motor imagery reduces cortical inhibitory activities during motor preparation, leading to shortened reaction time due to the repetitive recruitment of neural populations shared with motor imagery and movement preparation. To test this, we conducted a double-blind, sham-controlled study on 24 participants using neurofeedback training and pre- and post-training evaluation for simple reaction time tests and cortical inhibitory activity using short-interval intracortical inhibition (SICI). The results showed that veritable neurofeedback training effectively enhanced SMR-ERD in healthy male and female participants, accompanied by reduced simple reaction times and pre-movement SICI. Furthermore, SMR-ERD changes correlated with changes in pre-movement cortical disinhibition, and the disinhibition magnitude correlated with behavioral changes. These results suggest that SMR-neurofeedback modulates cortical inhibitory circuits during movement preparation, thereby enhancing motor performance.

Study protocol of a randomised controlled trial of the effects of near-infrared spectroscopy neurofeedback training coupled with virtual reality technology in children with ADHD

BackgroundThe rise in the number of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) highlights the need for effective interventions targeting attentional control. Although recent research has demonstrated the potential of neurofeedback...

Exploring neurofeedback as a therapeutic intervention for subjective cognitive decline.

This study addresses the pressing issue of subjective cognitive decline in aging populations by investigating neurofeedback (NFB) as a potential early therapeutic intervention. By evaluating the efficacy of individualised NFB training compared to standard protocols, tailored to each participant's EEG profile, it provides novel insights into personalised treatment approaches. The incorporation of innovative elements and rigorous analytical techniques contributes to advancing our understanding of NFB's modulatory effects on EEG frequencies and cognitive function in aging individuals. In the context of an aging population, concerns surrounding memory function become increasingly prevalent, particularly as individuals transition into middle age and beyond. This study investigated neurofeedback (NFB) as a potential early therapeutic intervention to address subjective cognitive decline (SCD) in aging populations. NFB, a biofeedback technique utilising a brain-computer interface, has demonstrated promise in the treatment of various neurological and psychological conditions. Here, we evaluated the efficacy of individualised NFB training, tailored to each participant's EEG profile, compared to a standard NFB training protocol aimed at increasing peak alpha frequency power, in enhancing cognitive function among individuals experiencing SCD. Our NFB protocol incorporated innovative elements, including the implementation of a criterion for learning success to ensure consistent achievement levels by the conclusion of the training sessions. Additionally, we introduced a non-learner group to account for individuals who do not demonstrate the expected proficiency in NFB regulation. Analysis of electroencephalographic (EEG) signals during NFB sessions, as well as before and after training, provides insights into the modulatory effects of NFB on EEG frequencies. Contrary to expectations, our rigorous analysis revealed that the ability of individuals with SCD to modulate EEG signal power and duration at specific frequencies was not exclusive to the intended frequency target. Furthermore, examination of EEG signals recorded using a high-density EEG showed no discernible alteration in signal power between pre- and post-NFB training sessions. Similarly, no significant effects were observed on questionnaire scores when comparing pre- and post-NFB training assessments.

Noninvasive brain–computer interfaces for children with neurodevelopmental disorders: Attention deficit hyperactivity disorder and autism spectrum disorder

Background:Brain–computer interface (BCI)-mediated neurofeedback training (BCI-NFT) has emerged as a highly promising treatment in the field of neurorehabilitation. Many previous studies have demonstrated the efficacy of BCI techniques in clinical rehabilitation, but children are largely neglected in BCI research. Purpose:This systematic review aimed to synthesize existing studies from technical and clinical application perspectives to identify the current state of research on noninvasive brain–computer interface (NBCI) technology in children with two major neurodevelopmental disorders, autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Methods:Five relevant electronic databases were searched (PubMed, Web of Science, the Cochrane Library, Embase, and the Cumulative Index of Nursing and Allied Health Literature). The publication dates ranged from the inception of each database to June 2024. Randomized controlled trials (RCTs) investigating the use of NBCI technology in children with ASD or ADHD were included. Manual searches of the clinical trial registry platforms and the reference lists of reviews related to the study topic were also conducted. Two independent reviewers performed the literature screening, data extraction, and risk of bias assessment. Results:A total of 24 RCTs involving 1998 children with ASD or ADHD were included in this systematic review. With respect to input brain signals, functional magnetic resonance imaging (fMRI) (4.2%), electroencephalography (EEG) combined with fMRI (4.2%), and EEG combined with galvanic skin response (GSR) sensors (4.2%) were utilized in one study each. Seven studies employed EEG combined with electrooculogram (EOG) (29.1%), and the remaining fourteen studies used EEG alone (58.3%). Compared with those of the controls, significant improvements in both behavioral aspects and brain activity in patients were observed in eleven studies (45.8%). NBCI technology has a positive effect on both the behavioral and brain activity levels of children with ASD or ADHD, while it still faces challenges in the paediatric population, particularly in terms of signal processing and the unique cognitive and physiological developmental stages of children, which may complicate the application of these technologies in this population. Conclusion:It demonstrated that there has a high potential for NBCI application in the field of neurodevelopmental disorders. Future research should focus on developing advanced machine learning algorithms to improve neural signal decoding capabilities and on creating child-appropriate application paradigms to explore the long-term efficacy of these algorithms.

Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.

Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism spectrum disorder (ASD), while studies have also demonstrated that mirror neuron mu rhythm-based EEG neurofeedback training improves the behavioral functioning of individuals with ASD. This study aimed to test the effects of a wearable mu rhythm neurofeedback training system based on machine learning algorithms for children with autism. A randomized, placebo-controlled study was carried out on 60 participants aged 3 to 6 years who were diagnosed with autism, at two center-based intervention sites. The neurofeedback group received active mu rhythm neurofeedback training, while the control group received a sham neurofeedback training. Other behavioral intervention programs were similar between the two groups. After 60 sessions of treatment, both groups showed significant improvements in several domains including language, social and problem behavior. The neurofeedback group showed significantly greater improvements in expressive language (P=0.013) and cognitive awareness (including joint attention, P=0.003) than did the placebo-controlled group. Artificial intelligence-powered wearable EEG neurofeedback, as a type of brain-computer interface application, is a promising assistive technology that can provide targeted intervention for the core brain mechanisms underlying ASD symptoms.

EEG and brain network analysis in the early diagnosis of Alzheimer's disease

Abstract. The early diagnosis of Alzheimers Disease (AD) is critical for effective intervention and improved patient outcomes. This paper explores the role of electroencephalography (EEG) and brain network analysis in the early detection of AD, with a particular focus on neurofeedback training as a therapeutic intervention. We review the characteristic changes in EEG signals, such as decreased alpha power and increased theta power, and their implications for identifying early cognitive decline. Functional connectivity analysis using coherence and phase synchronization methods reveals disruptions in key brain networks, providing valuable biomarkers for early AD. We also discuss quantitative EEG (qEEG) techniques, including power spectral density and fractal dimension, which enhance diagnostic accuracy. Additionally, mathematical models like the Kuramoto model offer insights into network dynamics and predict disease progression. Integrating EEG with other diagnostic tools, such as MRI and PET, creates a comprehensive multimodal approach. This integrative framework enhances early diagnosis and informs personalized treatment strategies, representing a significant advancement in the field of AD research and clinical practice.

Controlling Virtual Reality With Brain Signals: State of the Art of Using VR-Based Feedback in Neurofeedback Applications.

The rapid progress of commercial virtual reality (VR) technology, open access to VR development software as well as open-source instructions for creating brain-VR interfaces have increased the number of VR-based neurofeedback (NF) training studies. Controlling a VR environment with brain signals has potential advantages for NF applications. More entertaining, multimodal and adaptive virtual feedback modalities might positively affect subjective user experience and could consequently enhance NF training performance and outcome. Nevertheless, there are certain pitfalls and contraindications that make VR-based NF not suitable for everyone. In the present review, we summarize applications of VR-based NF and discuss positive effects of VR-based NF training as well as contraindications such as cybersickness in VR or age- and sex-related differences. The existing literature implies that VR-based feedback is a promising tool for the improvement of NF training performance. Users generally rate VR-based feedback more positively than traditional 2D feedback, albeit to draw meaningful conclusions and to rule out adverse effects of VR, more research on this topic is necessary. The pace in the development of brain-VR synchronization furthermore necessitates ethical considerations on these technologies.

Enhancing mathematical learning outcomes through a low-cost single-channel BCI system

This study investigates the effectiveness of a Low-Cost Single-Channel BCI system in improving mathematical learning outcomes, self-efficacy, and alpha power in university students. Eighty participants were randomly assigned to either a BCI group receiving real-time neurofeedback based on alpha rhythms or a sham feedback group. Results showed that the BCI group had significantly higher mathematical performance, self-efficacy, and alpha power compared to the sham feedback group. Mathematics performance, alpha wave intensity, and self-efficacy showed significant positive correlations after training, indicating that neurofeedback training may have promoted their interaction and integration. These findings demonstrate the potential of BCI technology in enhancing mathematical learning outcomes and highlight the importance of considering pre-test performance and self-efficacy in predicting learning outcomes, with implications for personalized learning interventions and the integration of BCI technology in educational settings.

Neurofeedback to enhance sleep quality and insomnia: a systematic review and meta-analysis of randomized clinical trials.

This systematic review and meta-analysis of randomized-clinical trials aims to analyze the effect of interventions incorporating surface neurofeedback techniques on self-perceived sleep quality and insomnia in patients with or without sleep disturbances. The review was completed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and was deposited in the Prospero international prospective registry of systematic reviews (CRD42024528401). Seven clinical trials with different main outcomes but with pre-post intervention records of self-perceived sleep quality or insomnia symptoms assessed by questionnaires met our inclusion criteria, including a publication date within the last 10 years. Five trials investigated sleep quality through scores on the Pittsburgh sleep quality Index (PSQI) and three trials signs of insomnia severity assessed with validated scales. The methodological quality of the included studies was assessed using the Cochrane Collaboration's tool for assessing the risk of bias and showed a high quality of them. A total of 5 studies that evaluated sleep quality with the PSQI total score were included in the meta-analysis. The results revealed that control conditions succeeded in improving PSQI-assessed sleep quality more than the analyzed Neurofeedback interventions (PSQI total score 0.57; 95% CI 0.13 to 1.01; p = 0.01). On the other hand, a total of 3 studies that evaluated insomnia severity with various insomnia scales were included in the meta-analysis The results revealed that neither the NF interventions nor the control conditions show a favorable outcome relative to each other (-0.13; 95% CI -0.44 to 0.18; p = 0.41). The interventions studied mostly apply a neurofeedback training protocol based on maintaining alpha waves in a range between 8 and 12 Hz, with electrode positioning in the frontal area or in the sensorimotor cortex and with a number of neurofeedback sessions ranging from 8 to 20 sessions. The meta-analysis showed that interventions incorporating surface neurofeedback do not produce additional benefits in self-perception of sleep quality or insomnia compared to a wide variety of control conditions including cognitive behavioral treatment or other biofeedback modalities. PROSPERO - International prospective register of systematic reviews - CRD42024528401 https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=528401.

Effects of Frontal-Midline Theta Neurofeedback with Different Training Directions on Goal-Directed Attentional Control.

As a significant component of executive function, goal-directed attentional control is crucial for cognitive processing and is closely linked to frontal-midline theta (FMT) rhythms. However, how up-regulation and down-regulation of FMT through neurofeedback training (NFT) impact goal-directed attention control remains unclear, especially for both short-term and long-lasting effects. Therefore, this study employed a single-blind sham-controlled between-subject design to answer this question. Forty-seven healthy adults were randomly assigned to the up-regulation, down-regulation, or sham control groups. Each group underwent one NFT session per day at the Fz electrode site for four consecutive days. All participants completed a visual search task before, immediately after the first, after the final, and one week following the last NFT session. The down-regulation group significantly reduced FMT activity during NFT and in the resting state (p < = 0.038), while the up-regulation group only showed an upward trend during the training phase (r = 0.721, p = 0.002). The behavioral performance showed no significant improvement in any group (p > 0.05). Importantly, the FMT learning efficacy in the up-regulation group revealed a significantly negative correlation with the change in switch cost (r = -0.602, p = 0.046). These findings suggest a close link between the up-regulation efficacy of FMT rhythms and goal-directed attentional control. In educational or clinical settings, it would be desirable to improve goal-directed attention through enhancement of FMT up-regulation efficacy of NFT in future work.

Shifts in task absorption during decision-making episodes

In today’s rapidly evolving technological landscape, this study investigates the impact of new technology on organizational work dynamics. By integrating the Job Demands-Resources theory and the Episodic Process Model, we examine the cognitive mechanisms that influence task performance during decision-making episodes. Our research focuses on episodic job demands and resources, emphasizing the mediating role of task absorption in the relationship between these factors and task performance. Our findings reveal that episodic job resources positively affect task absorption and performance, while job demands moderate the relationship between job resources and task absorption. Employing the Beer Distribution Game alongside neuroscience-based eye-tracking techniques, we analyze visual attention dynamics during decision-making episodes, providing novel insights into the interplay between task absorption and task performance. Theoretically, our study highlights the significance of task absorption in understanding how episodic job resources impact decision-making performance. Practically, our results advocate for the implementation of decision-making-focused skills development through physiological measures such as neurofeedback training programs. This research underscores the importance of individual time management in enhancing task performance, contributing to a nuanced understanding of job resources, job demands, and task absorption in the context of technological transformation.